Experimental Investigations and MD Simulation on Nanoparticle-Enhanced CO<sub>2</sub>-Responsive Foam (NECRF): Implications on CO<sub>2</sub>-EOR
Bibliographic record
Abstract
CO 2 -responsive foam (CRF) is a highly promising candidate for CO 2 -enhanced oil recovery (CO 2 -EOR) because it displays higher stability than the surfactant-stabilized foam owing to the formation of robust wormlike micelles (WLMs) upon exposure to CO 2 . In this work, the nanoparticle-enhanced CO 2 -responsive foam (NECRF) was properly prepared using lauryl ether sulfate sodium (LES)/diethylenetriamine/nano-SiO 2, and its interfacial properties and EOR potential were experimentally and numerically assessed, aiming to explore the feasibility and effectiveness of NECRF as a novel CO 2 -EOR technique. It was found that the interfacial expansion elastic modulus increased 6-fold after CO 2 stimulation. The modulus continued to increase with the introduction of nano-SiO 2 owing to the pronounced synergistic effect of WLMs and nanoparticles. In addition to increasing the viscosity of the foaming liquid, WLMs and nano-SiO 2 enhanced the shearing resistance of the NECRF as well. Calculations demonstrated that both the coarsening rate and the size distribution uniformity coefficient of NECRF were markedly lower than that of the LES foam, which subsequently inhibited NECRF decay and greatly improved its dynamic stability. Besides, molecular dynamics simulation revealed that adding inorganic salts to NECRF could notably enhance the foaming performance due to the intensified hydration of surfactant head groups and reduced binding energy of neighboring molecules. Nuclear magnetic resonance-assisted core flooding experiments validated the exceptional capacity of NECRF to sweep the low-permeability region and improve the conformance profile. Overall, these findings may provide valuable insights into the development and application of novel materials and strategies for the CO 2 -EOR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".